The Timeless Rivalry: Ronaldo vs Messi

An investigation tracking Messi and Ronaldo’s performance trends

Project description

Author

Affiliation

Mobolaji Adewale, Narasimha Vardhan Rachaputi, Pradnya Raut, Praveen Kumar Pappala, Tushar Kant Singh, Usama Ahmed

School of Information, University of Arizona

Introduction

In the world of football, the names Cristiano Ronaldo and Lionel Messi resonate as symbols of unparalleled talent and sporting prowess. Their enduring rivalry has fueled debates among fans, pundits, and analysts, each vying to determine the superior player. However, amidst the fervour of subjective opinions, lies an opportunity to uncover objective truths through data-driven analysis. This project embarks on a journey to delve into the goal-scoring exploits of Ronaldo and Messi, employing statistical methods to dissect their performances across diverse dimensions. By meticulously examining their records spanning countries, competitions, opponents, matchdays, and goal times, this endeavour aims to provide a comprehensive understanding of their impact on the beautiful game.

Project Overview

The project unfolds in several phases, commencing with the meticulous wrangling of datasets to ensure data cleanliness and coherence. Following this, bespoke functions are crafted to calculate and visualize the number of goals scored by each player based on specified criteria, such as opponent, competition, matchday, and goal time. Leveraging these functions, the data is analysed and visualized to unearth insights into Ronaldo and Messi’s goal-scoring prowess, facilitating a nuanced comparison of their performances over the years.

Methodology

The methodology encompasses a systematic approach to data wrangling, analysis, and visualization. It begins with the loading of necessary libraries and datasets, followed by rigorous data cleaning and preparation to ensure accuracy and consistency. Key functions are then defined to compute and visualize the goal-scoring data, enabling comprehensive exploration across various dimensions. Through data analysis and visualization techniques, including bar charts and maps, the project illuminates the spatial and temporal dynamics of Ronaldo and Messi’s performances, offering valuable insights into their contributions to the sport.

Topic One

Animations

Animations, cont.

Interactive

Discussion

First animation is an animation created using gganimate. It is a highlighting countries where Messi and Ronaldo scored goals for a particular year over the period from 2005 to 2022. It also displays total number of goals scored by each player in all matches in that year

Second animation is also similar to first one displaying countries where goals were scored in a year all over the world by the two but zooming only Europe and Russia.

In year 2012 Messi scored highest goals of 74 whereas in 2013, Ronaldo scored highest goals of 58.

Both players scored higher goals compared to the other for 8 years each.

Messi scored higher goals than Ronaldo in years viz. 2009-2010, 2012, 2016-2019, 2022 whereas Ronaldo scored higher than Messi in the years 2005-2008,2013-2015, 2020

The total goals scored by Messi in there years are 652 whereas goals scored by Ronaldo in the same period are 654.

From 2005 to 2022, globally, Messi has scored more goals than Ronaldo in 11 countries, while Ronaldo has outscored Messi in 13 countries out of all matches played by both players.

So the performance of both players looks almost comparable in terms of goals scored with Ronaldo scoring marginally higher total scores than Messi

Topic Two

Discussion

In the second part of the project delves deeper into the goal-scoring prowess of Ronaldo and Messi, examining their performance across various dimensions such as countries, competitions, opponents, matchdays, and goal times. It employs data wrangling techniques, function definitions, and comprehensive analysis to provide a nuanced comparison of their performances, offering insights into their respective careers and contributions to the sport.

Limitations

The comparison between both players spans the available data from 2005 to 2022, covering a duration of 18 years. As the dataset grows larger, computational resources and processing times required for data analysis and visualization may increase significantly, posing scalability challenges, particularly when dealing with extensive historical data or incorporating real-time updates. The project focuses primarily on goal-scoring statistics and may not capture the entirety of Ronaldo and Messi’s contributions to football. Other aspects of their gameplay, such as assists, dribbles, defensive actions, etc., are not considered, which may provide a more holistic understanding of their performance.

--- title: "The Timeless Rivalry: Ronaldo vs Messi" subtitle: "An investigation tracking Messi and Ronaldo's performance trends" author: - name: "Mobolaji Adewale, Narasimha Vardhan Rachaputi, Pradnya Raut, Praveen Kumar Pappala, Tushar Kant Singh, Usama Ahmed" affiliations: - name: "School of Information, University of Arizona" description: "Project description" format: html: code-tools: true code-overflow: wrap embed-resources: true editor: visual execute: warning: false echo: false message: false --- ## Introduction In the world of football, the names Cristiano Ronaldo and Lionel Messi resonate as symbols of unparalleled talent and sporting prowess. Their enduring rivalry has fueled debates among fans, pundits, and analysts, each vying to determine the superior player. However, amidst the fervour of subjective opinions, lies an opportunity to uncover objective truths through data-driven analysis. This project embarks on a journey to delve into the goal-scoring exploits of Ronaldo and Messi, employing statistical methods to dissect their performances across diverse dimensions. By meticulously examining their records spanning countries, competitions, opponents, matchdays, and goal times, this endeavour aims to provide a comprehensive understanding of their impact on the beautiful game. ## Project Overview The project unfolds in several phases, commencing with the meticulous wrangling of datasets to ensure data cleanliness and coherence. Following this, bespoke functions are crafted to calculate and visualize the number of goals scored by each player based on specified criteria, such as opponent, competition, matchday, and goal time. Leveraging these functions, the data is analysed and visualized to unearth insights into Ronaldo and Messi's goal-scoring prowess, facilitating a nuanced comparison of their performances over the years. ## Methodology The methodology encompasses a systematic approach to data wrangling, analysis, and visualization. It begins with the loading of necessary libraries and datasets, followed by rigorous data cleaning and preparation to ensure accuracy and consistency. Key functions are then defined to compute and visualize the goal-scoring data, enabling comprehensive exploration across various dimensions. Through data analysis and visualization techniques, including bar charts and maps, the project illuminates the spatial and temporal dynamics of Ronaldo and Messi's performances, offering valuable insights into their contributions to the sport. ## Topic One ```{r Loading-Datasets} #| fig-width: 15 #| fig-height: 15 pacman::p_load(tidyverse, readr, stringr, sf, rnaturalearth, rnaturalearthdata, ggspatial, ggh4x, ggthemes, data.table, plotly, grDevices) # Load data ronaldo <- read_csv("data/cr7.csv") messi <- read_csv("data/messi.csv") # Data preprocessing ronaldo <- ronaldo %>% mutate( Year = paste0("20", str_sub(Date, -2, -1)) ) messi <- messi %>% mutate( Year = paste0("20", str_sub(Date, -2, -1)) ) ronaldo <- ronaldo %>% filter(Season != "02/03" & Season != "03/04") # RETRIEVE GEOMETRIES OF WORLD AND EUROPE world <- ne_countries(scale = "medium", returnclass = "sf") europe <- ne_countries(scale = "medium", returnclass = "sf", continent = "europe") combinedMessiRonaldo <- bind_rows(ronaldo,messi, .id = "Player") combinedMessiRonaldo <- combinedMessiRonaldo %>% mutate(Player = case_when( Player == 1 ~ "Ronaldo", Player == 2 ~ "Messi" )) combinedCopy <- combinedMessiRonaldo ``` ```{r Country-Data} #| fig-width: 15 #| fig-height: 15 # LINK OPPOSING TEAMS TO THEIR COUNTRIES countries <- data.frame(coun = c('Moreirense FC - Portugal', 'Boavista FC - Portugal', 'CD Estarreja - Portugal', 'FC Oliveira do Hospital - Portugal', 'Portsmouth FC - England', 'Manchester City - England', 'Tottenham Hotspur - England', 'Birmingham City - England', 'Aston Villa - England', 'Millwall FC - England', 'Southampton FC - England', 'Exeter City - England', 'Arsenal FC - England', 'Everton FC - England', 'Fulham FC - England', 'Newcastle United - England', 'Debreceni VSC - Hungary', 'Middlesbrough FC - England', 'West Bromwich Albion - England', 'Bolton Wanderers - England', 'Wigan Athletic - England', 'Charlton Athletic - England', 'Reading FC - England', 'Watford FC - England', 'AS Roma - Italy', 'AC Milan - Italy', 'Sporting CP - Portugal', 'Dynamo Kyiv - Ukraine', 'Blackburn Rovers - England', 'Derby County - England', 'Sunderland AFC - England', 'West Ham United - England', 'Olympique Lyon - France', 'Liverpool FC - England', 'Chelsea FC - England', 'Hull City - England', 'Stoke City - England', 'Gamba Osaka - Japan', 'FC Internazionale - Italy', 'FC Porto - Portugal', 'Deportivo de La Coruña - Spain', 'RCD Espanyol Barcelona - Spain', 'FC Zürich - Switzerland', 'Xerez CD - Spain', 'Villarreal CF - Spain', 'Olympique Marseille - France', 'UD Almería - Spain', 'Real Zaragoza - Spain', 'Malaga CF - Spain', 'CD Tenerife - Spain', 'Sevilla FC - Spain', 'Real Valladolid CF - Spain', 'Getafe CF - Spain', 'Racing Santander - Spain', 'Valencia CF - Spain', 'CA Osasuna - Spain', 'RCD Mallorca - Spain', 'Athletic Bilbao - Spain', 'Hercules CF - Spain', 'Real Murcia CF - Spain', 'Ajax Amsterdam - Netherlands', 'AJ Auxerre - France', 'Levante UD - Spain', 'Atletico de Madrid - Spain', 'Real Sociedad - Spain', 'FC Barcelona - Spain', 'Rayo Vallecano - Spain', 'Sporting Gijón - Spain', 'SD Ponferradina - Spain', 'Granada CF - Spain', 'CSKA Moscow - Russia', 'Real Betis Balompie - Spain', 'APOEL Nicosia - Cyprus', 'Bayern Munich - Germany', 'Celta de Vigo - Spain', 'Borussia Dortmund - Germany', 'Manchester United - England', 'Galatasaray - Turkey', 'Elche CF - Spain', 'FC Copenhagen - Denmark', 'Juventus FC - Italy', 'FC Schalke 04 - Germany', 'Córdoba CF - Spain', 'FC Basel 1893 - Switzerland', 'Ludogorets Razgrad - Bulgaria', 'SD Eibar - Spain', 'Shakhtar Donetsk - Ukraine', 'Malmö FF - Sweden', 'UD Las Palmas - Spain', 'VfL Wolfsburg - Germany', 'Deportivo Alaves - Spain', 'CF America - Mexico', 'Kashima Antlers - Japan', 'Al-Jazira (Abu Dhabi) - United Arab Emirates', 'Grêmio Foot-Ball Porto Alegrense - Brazil', 'Paris Saint-Germain - France', 'Girona FC - Spain', 'US Sassuolo - Italy', 'Frosinone Calcio - Italy', 'Udinese Calcio - Italy', 'Genoa CFC - Italy', 'Empoli FC - Italy', 'SPAL - Italy', 'ACF Fiorentina - Italy', 'Torino FC - Italy', 'Atalanta BC - Italy', 'UC Sampdoria - Italy', 'SS Lazio - Italy', 'Parma Calcio 1913 - Italy', 'Inter Milan - Italy', 'SSC Napoli - Italy', 'Hellas Verona - Italy', 'Bayer 04 Leverkusen - Germany', 'Bologna FC 1909 - Italy', 'Cagliari Calcio - Italy', 'US Lecce - Italy', 'Spezia Calcio - Italy', 'Ferencvarosi TC - Hungary', 'FC Crotone - Italy', 'BSC Young Boys - Switzerland', 'Norwich City - England', 'Burnley FC - England', 'Brighton & Hove Albion - England', 'Brentford FC - England', 'FC Sheriff - Moldova', 'Albacete Balompie - Spain', 'Panathinaikos Athens - Greece', 'SV Werder Bremen - Germany', 'Real Madrid - Spain', 'Recreativo Huelva - Spain', 'Deportivo de La Coruña - Spain', 'Gimnastic de Tarragona - Spain', 'VfB Stuttgart - Germany', 'UD Almería - Spain', 'Rangers FC - Scotland', 'Celtic FC - Scotland', 'Benidorm CD - Spain', 'CD Numancia - Spain', 'Cultural Leonesa - Spain', 'CF Atlante - Mexico', 'Club Estudiantes de La Plata - Argentina', 'Club Atletico de Ceuta - Spain', 'BATE Borisov - Belarus', 'FC Viktoria Plzen - Czech Republic', 'Santos FC - Brazil', 'Spartak Moscow - Russia', 'CA River Plate - Argentina', 'CD Leganes - Spain', 'Borussia Mönchengladbach - Germany', 'Olympiacos Piraeus - Greece', 'SD Huesca - Spain', 'PSV Eindhoven - Netherlands', 'SK Slavia Prague - Czech Republic', 'Cadiz CF - Spain', 'RB Leipzig - Germany', 'FC Nantes - France', 'Club Brugge KV - Belgium', 'LOSC Lille - France', 'FC Lorient - France', 'RC Lens - France', 'Montpellier - France', 'Clermont Foot - France', 'Maccabi Haifa - Israel', 'OGC Nice - France', 'SL Benfica - Portugal', 'AC Ajaccio - France', 'ESTAC Troyes - France' )) countries <- separate(countries, coun, into = c("Opponent", "Country"), sep = " - ") # MATCH TEAMS SCORED AGAINSTS TO COUNTRIES combinedMessiRonaldo <- left_join(combinedMessiRonaldo, countries, by = "Opponent") combinedCopy <- left_join(combinedCopy, countries, by = "Opponent") combinedMessiRonaldo <- combinedMessiRonaldo %>% mutate(Country = case_when( Country == "England" ~ "United Kingdom", Country == "Wales" ~ "United Kingdom", Country == "Scotland" ~ "United Kingdom", Country == "Czech Republic" ~ "Czechia", T ~ Country )) # ADD COUNTRY GEOMETRIES TO COUNTRIES SCORED AGAINST worldMergedWithMessiRonaldo <- left_join(combinedMessiRonaldo, world, by = c("Country" = "geounit")) europeMergedWithMessiRonaldo <- inner_join(combinedMessiRonaldo, europe, by = c("Country" = "geounit")) worldMerged <- worldMergedWithMessiRonaldo %>% filter(!is.na(Country)) %>% select(c(1:16, 20, 184)) europeMerged <- europeMergedWithMessiRonaldo %>% filter(!is.na(Country)) %>% select(c(1:16, 20, 184)) mapTestingCode <- ggplot() + geom_sf(data = world, aes(geometry = geometry)) + geom_sf(data = worldMerged, aes(geometry = geometry, fill = is.na(Player))) + theme_minimal() + theme() + scale_fill_manual(values = c("gold", "azure")) mapTestingCode2 <- ggplot() + geom_sf(data = europe, aes(geometry = geometry)) + geom_sf(data = europeMerged, aes(geometry = geometry, fill = is.na(Player))) + theme_minimal() + theme() + scale_fill_manual(values = c("gold", "azure")) ``` ### Animations ```{r Animations} #| fig-asp: 0.618 #| fig-width: 20 pacman:: p_load(gganimate, plotly, maps, ggthemes, ggrepel, av, rworldmap, gifski) # UNUSED ANIMATION simpleAnimationOne <- ggplot() + geom_sf(data = world, aes(geometry = geometry)) + theme_minimal()+ geom_sf(data = worldMerged, aes(geometry = geometry, fill = Player)) + facet_wrap2(vars(Player), nrow = 2) + ggtitle("Year: {closest_state}") + theme(legend.position = "none") + transition_states(Year, transition_length = 1, state_length = 5, T) #anim <- anim + enter_fade() + exit_fade() #animate(anim, renderer = av_renderer("out.mp4"), height = 800, width = 1000, res = 500) # SUMMARISE GOALS SCORED AND COUNTRIES SCORED AGAINST worldSquashed <- worldMerged %>% group_by(Year, Country, Player, geometry) %>% summarise(goals = n()) europeSquashed <- europeMerged %>% group_by(Year, Country, Player, geometry) %>% summarise(goals = n()) # DID RONALDO AND MESSI SCORE IN SAME COUNTRY IN SAME YEAR? worldSquashed2 <- worldSquashed %>% group_by(Year, Country )%>% mutate(plays = n()) %>% mutate(colr = case_when( Player == "Messi" & plays == 1 ~ "Messi", Player == "Ronaldo" & plays == 1 ~ "Ronaldo", plays == 2 ~ "Both", T ~ "Neither" )) europeSquashed2 <- europeSquashed %>% group_by(Year, Country )%>% mutate(plays = n()) %>% mutate(colr = case_when( Player == "Messi" & plays == 1 ~ "Messi", Player == "Ronaldo" & plays == 1 ~ "Ronaldo", plays == 2 ~ "Both", T ~ "Neither" )) worldSquashed3 <- worldSquashed2 %>% filter(colr == "Both") europeSquashed3 <- europeSquashed2 %>% filter(colr == "Both") ``` ```{r results='hide'} #TO RETRIEVE CENTER POINTS FOR COUNTRY LABELS countryDataJoinedToMap <- suppressMessages(joinCountryData2Map(combinedCopy, joinCode = "NAME", nameJoinColumn = "Country", verbose = F)) country_coord <- data.frame(coordinates(countryDataJoinedToMap), stringsAsFactors = FALSE) country_coord2 <- country_coord %>% mutate(country = row.names(country_coord)) ``` ```{r} #| fig-asp: 0.618 #| fig-width: 20 ## ADDS CENTER POINTS OF COUNTRIES TO COUNTRY INFORMATION worldSquashed4 <- worldSquashed2 %>% left_join(country_coord2, by = c("Country" = "country")) europeSquashed4 <- europeSquashed2 %>% left_join(country_coord2, by = c("Country" = "country")) # SPLITS MESSIS AND RONALDOS GOALS worldMessi <- subset(worldSquashed4, Player %in% "Messi") %>% group_by(Year) %>% summarise(goalsScored = sum(goals)) europeMessi <- subset(europeSquashed4, Player %in% "Messi") %>% group_by(Year) %>% summarise(goalsScored = sum(goals)) worldRonaldo <- subset(worldSquashed4, Player %in% "Ronaldo") %>% group_by(Year) %>% summarise(goalsScored = sum(goals)) europeRonaldo <- subset(europeSquashed4, Player %in% "Ronaldo") %>% group_by(Year) %>% summarise(goalsScored = sum(goals)) # ANIMATION FOR WORLD !!! complexAnimationWorld <- ggplot() + geom_sf(data = world, aes(geometry = geometry)) + theme_minimal()+ geom_sf(data = worldSquashed2, aes(geometry = geometry, fill = colr, col = colr)) + coord_sf(xlim = c(-110, 150), ylim = c(-50, 70)) + theme(legend.position = "left", axis.title = element_blank(), axis.text = element_blank(), panel.grid = element_blank(), panel.border = element_rect(colour = "black", fill = NA), panel.background = element_rect(fill = "azure"), text = element_text(size = 20) , plot.title = element_text(size = 20), plot.subtitle = element_text(size = 20) ) + labs(fill = "Player", title="Goals scored by Ronaldo and Messi",subtitle="Year: {closest_state}") + guides(color = "none") + scale_color_manual(values = c("Both" = "purple", "Ronaldo" = "#DA020E", "Messi" = "#004996")) + scale_fill_manual(values = c("Both" = "#E39FF6", "Ronaldo" = "#DA291C", "Messi" = "#6CACE4")) + geom_text(data=worldSquashed4, aes(label = Country, x = `X1`, y = `X2`), size = 3) + geom_text(data = worldMessi, aes(label = paste0("Messi\nGoals\n", goalsScored)), x = -110, y = -40, size = 10)+ geom_text(data = worldRonaldo, aes(label = paste0("Ronaldo\nGoals\n", goalsScored)), x = -20, y = -40, size = 10)+transition_states(Year, transition_length = 1, state_length = 40, T) #complexAnimationWorld #animate(complexAnimation, height = 1000, width = 1300, duration = 120, renderer = gifski_renderer()) animate(complexAnimationWorld, duration = 60, renderer = gifski_renderer()) #animate(complexAnimationWorld, duration = 60, renderer = av_renderer("data/world.mp4"),height = 13, width = 20, units = "in", res = 90) ``` ### Animations, cont. ```{r Animations-2} #| fig-asp: 0.618 #| fig-width: 20 # ANIMATION FOR EUROPE !!! complexAnimationEurope <- ggplot() + geom_sf(data = europe, aes(geometry = geometry)) + theme_minimal()+ geom_sf(data = europeSquashed2, aes(geometry = geometry, fill = colr)) + ggtitle("Year: {closest_state}") + coord_sf(xlim = c(-10, 115), ylim = c(25, 75)) + theme(legend.position = "left", axis.title = element_blank(), axis.text = element_blank(), panel.grid = element_blank(), panel.border = element_rect(colour = "black", fill = NA), panel.background = element_rect(fill = "azure"), text = element_text(size = 20), plot.title = element_text(size = 20), plot.subtitle = element_text(size = 20)) + labs(fill = "Player", title="Goals scored by Ronaldo and Messi (Europe and Russia)",subtitle="Year: {closest_state}") + guides(color = "none") + scale_color_manual(values = c("Both" = "purple", "Ronaldo" = "#DA020E", "Messi" = "#004996")) + scale_fill_manual(values = c("Both" = "#E39FF6", "Ronaldo" = "#DA291C", "Messi" = "#6CACE4")) + geom_text(data=europeSquashed4, aes(label = Country, x = `X1`, y = `X2`), size = 3) + geom_text(data = europeMessi, aes(label = paste0("Messi\nGoals\n", goalsScored)), x = 90, y = 30, size = 10)+ geom_text(data = europeRonaldo, aes(label = paste0("Ronaldo\nGoals\n", goalsScored)), x = 110, y = 30, size = 10) + transition_states(Year, transition_length = 1, state_length = 40, T) #complexAnimationEurope animate(complexAnimationEurope, duration = 60, renderer = gifski_renderer()) #animate(complexAnimationEurope, duration = 60, renderer = av_renderer("data/europe.mp4"),height = 13, width = 20, units = "in", res = 90) # UNUSED ANIMATION simpleAnimation2 <- ggplot() + geom_sf(data = world, aes(geometry = geometry)) + theme_minimal()+ geom_sf(data = worldMerged, aes(geometry = geometry, fill = )) + ggtitle("Year: {closest_state}") + theme(legend.position = "none") + transition_states(Year, transition_length = 1, state_length = 8, T) #animate(anim, renderer = av_renderer("out2.mp4"), height = 1000, width = 1300) #animate(map2) ``` ### Interactive ```{r} #| fig-asp: 0.618 #| fig-width: 20 # Data preprocessing worldChanged <- worldSquashed %>% ungroup() %>% select(!Year) %>% group_by(Player, Country) %>% summarise(scored = sum(goals)) worldChanged2 <- left_join(worldChanged, select(world, c("geounit", "geometry")), by = c("Country" = "geounit")) worldChanged3 <- worldChanged2 %>% mutate(messiGoals = case_when( Player == "Messi" ~ scored, T ~ 0 ), ronaldoGoals = case_when( Player == "Ronaldo" ~ scored, T ~ 0 ) ) # Flattened the dataframe worldChanged4 <- worldChanged3 %>% group_by(Country, geometry) %>% summarise(mg = sum(messiGoals), rg = sum(ronaldoGoals)) %>% mutate(toolText = paste("Goals in ", Country, "\nMessi: ", mg, "\nRonaldo: ", rg) ) # Defined color palette pal <- colorRampPalette(c("maroon", "#FFF700")) # Plots plotlyOne <- ggplot() + geom_sf(data = world, aes(geometry = geometry)) + theme_minimal()+ geom_sf(data = worldChanged4, aes(geometry = geometry, fill = Country, text = toolText)) + coord_sf(xlim = c(-110, 150), ylim = c(-50, 70)) + theme(legend.position = "none", axis.title = element_blank(), axis.text = element_blank(), panel.grid = element_blank(), panel.border = element_rect(colour = "black", fill = NA), panel.background = element_rect(fill = "azure"), text = element_text(size = 20) , plot.title = element_text(size = 20), plot.subtitle = element_text(size = 20) ) + labs(fill = "Player", title="Goals scored by Ronaldo and Messi",subtitle="Year: {closest_state}") + guides(color = "none") + scale_color_manual(values = c("Both" = "purple", "Ronaldo" = "#DA020E", "Messi" = "#004996")) + scale_fill_manual(values = pal(length(unique(worldChanged4$Country)))) #scale_fill_manual(values = c("Both" = "#E39FF6", "Ronaldo" = "#DA291C", "Messi" = "#6CACE4")) #+ #geom_text(data=worldSquashed4, aes(label = Country, x = `X1`, y = `X2`), size = 3) #englandTest <- ggplot(data = subset(worldChanged2, Country == "United Kingdom")) + #geom_col(aes(x = Player, y = scored, fill = Player), colour = "black", linewidth = .4) #+ theme_minimal() + labs(y = "Goals") + theme(legend.position = "none", axis.title.x = #element_blank()) #englandTest #ggsave("images/uk.jpg", englandTest) # Formatting the interactive plot font <- list( family = "Arial", size = 25, color = "white") # "Roboto Condensed" label <- list( bgcolor = "purple" , bordercolor = "transparent", font = font ) # "#232F34") plotlyTwo <- ggplotly(plotlyOne, tooltip = 'text') %>% style(hoverlabel = label) %>% layout(font = font) plotlyTwo ``` ### Discussion First animation is an animation created using gganimate. It is a highlighting countries where Messi and Ronaldo scored goals for a particular year over the period from 2005 to 2022. It also displays total number of goals scored by each player in all matches in that year Second animation is also similar to first one displaying countries where goals were scored in a year all over the world by the two but zooming only Europe and Russia. In year 2012 Messi scored highest goals of 74 whereas in 2013, Ronaldo scored highest goals of 58. Both players scored higher goals compared to the other for 8 years each. Messi scored higher goals than Ronaldo in years viz. 2009-2010, 2012, 2016-2019, 2022 whereas Ronaldo scored higher than Messi in the years 2005-2008,2013-2015, 2020 The total goals scored by Messi in there years are 652 whereas goals scored by Ronaldo in the same period are 654. From 2005 to 2022, globally, Messi has scored more goals than Ronaldo in 11 countries, while Ronaldo has outscored Messi in 13 countries out of all matches played by both players. So the performance of both players looks almost comparable in terms of goals scored with Ronaldo scoring marginally higher total scores than Messi ## Topic Two ```{r functions, include = FALSE, echo = FALSE, warning = FALSE, message = FALSE} # Calculate Goals Function calculate_goals <- function(df,col){ x <- df |> mutate(Year = paste0("20", str_sub(df$Date, -2, -1)) ) |> filter(!is.na(Country) & Year %in% c(paste0("200",05:09),paste0("20",10:22))) |> group_by(.data[[col]],label) |> summarise(count = n()) |> arrange(-count) return(x) } # Plotter Function plotter <- function(filter, df) { b <- ggplot(df[df$plot_filter == filter, ], aes(x = plot_filter, y = count, fill = label)) + geom_bar(stat = "identity", position = "dodge") + theme_minimal() + labs(title = filter, x = "", y = "Number of Goals", fill = "Player") + theme(plot.title = element_text(hjust = 0.5), axis.text.x = element_blank(), panel.grid.minor = element_blank()) + scale_fill_manual(values = c("Ronaldo" = "#006200", "Messi" = "#43A1D5")) return(b) } ``` ```{r data_wrangling, include = FALSE, echo = FALSE, warning = FALSE, message = FALSE} messi$label <- "Messi" ronaldo$label <- "Ronaldo" df <- rbind(messi,ronaldo) df <- left_join(df, countries, by = "Opponent") # By Country/Region (Cannot use calculate_goals function because of the fix) by_region <- df |> mutate(Year = paste0("20", str_sub(df$Date, -2, -1)) ) |> filter(Venue == "A" & !is.na(Country) & Year %in% c(paste0("200",05:09),paste0("20",10:22))) |> group_by(Country,label) |> summarise(count = n()) |> arrange(-count) fix <- df |> mutate(Year = paste0("20", str_sub(df$Date, -2, -1)) ) |> filter(Venue == "H" & !is.na(Country) & Year %in% c(paste0("200",05:09),paste0("20",10:22))) |> group_by(label, Club) |> summarise(count_h = n()) |> arrange(-count_h) |> glimpse() by_region <- by_region |> mutate(count = case_when(Country == "Spain" & label == "Messi" ~ count + 383, Country == "Spain" & label == "Ronaldo" ~ count + 228, Country == "France" & label == "Messi" ~ count + 14, Country == "England" & label == "Ronaldo" ~ count + 87, Country == "Italy" & label == "Ronaldo" ~ count + 57, TRUE ~ count)) ## By Opponent by_opponent <- calculate_goals(df,'Opponent') ## By Competition by_competition <- calculate_goals(df,'Competition') ## By Matchday by_matchday <- calculate_goals(df,'Matchday') ## By Last Minute Goals by_lastminute <- calculate_goals(df,'Minute') by_lastminute <- by_lastminute |> mutate(Minute = case_when(grepl("^9|^10|^11|^12|^13",Minute) ~ ">90", grepl("^8",Minute) ~ ">80", grepl("^7",Minute) ~ ">70", grepl("^6",Minute) ~ ">60", grepl("^5",Minute) ~ ">50", grepl("^4",Minute) ~ ">40", grepl("^3",Minute) ~ ">30", grepl("^2",Minute) ~ ">20", TRUE ~ "<10")) |> group_by(Minute,label) |> summarise(count = n()) ``` ```{r html_configurations, include = FALSE, echo = FALSE, warning = FALSE, message = FALSE} ## Dropdown filters filter_region <- c("Spain","England","France","Germany","Italy", "Ukraine", "Netherlands", "Turkey") filter_competition <- c('LaLiga','UEFA Champions League','Copa del Rey') filter_opponent <- c("Manchester United", "Bayern Munich", "Liverpool FC", "Chelsea FC", "Juventus FC", "Paris Saint-Germain", "Arsenal FC", "Borussia Dortmund") filter_matchday <- c('Quarter-Finals','Semi-Finals','Final') filter_minute <- c(">90",">80") ## Make a combined data frame by_region <- by_region |> rename(plot_filter = Country) |> mutate(val = "Region") by_competition <- by_competition |> rename(plot_filter = Competition) |> mutate(val = "Competition") by_lastminute <- by_lastminute |> rename(plot_filter = Minute) |> mutate(val = "Goal Time") by_opponent <- by_opponent |> rename(plot_filter = Opponent) |> mutate(val = "Opponent") by_matchday <- by_matchday |> rename(plot_filter = Matchday) |> mutate(val = "Matchday") combined_df <- rbind(by_region,by_competition,by_lastminute,by_matchday,by_opponent) ``` ::: panel-tabset ### Country ```{r , echo=FALSE, warning=FALSE, results="asis"} # Create a dropdown menu without the "All" option cat('<select id="filterDropdown">') for (region in filter_region) { cat(sprintf('<option value="%s">%s</option>', region, region)) } cat('</select>') cat('<div id="plotContainer">') for (i in seq_along(filter_region)) { region <- filter_region[i] cat(sprintf('<div id="plot%d" class="plot-div" data-region="%s">', i, region)) print(plotter(region, combined_df)) cat('</div>') } cat('</div>') # JavaScript to handle dropdown change event cat(' <script> document.getElementById("filterDropdown").addEventListener("change", function() { var selectedValue = this.value; var plots = document.querySelectorAll(".plot-div"); plots.forEach(function(plot) { var region = plot.getAttribute("data-region"); if (region === selectedValue) { plot.style.display = "block"; } else { plot.style.display = "none"; } }); }); </script> ') ``` ### Competition ```{r , echo=FALSE, warning=FALSE, results="asis"} # Create a dropdown menu for competitions cat('<select id="competitionDropdown">') for (competition in filter_competition) { cat(sprintf('<option value="%s">%s</option>', competition, competition)) } cat('</select>') cat('<div id="plotContainer">') for (i in seq_along(filter_competition)) { competition <- filter_competition[i] cat(sprintf('<div id="plot%d" class="plot-div" data-competition="%s">', i, competition)) print(plotter(competition, combined_df)) cat('</div>') } cat('</div>') # JavaScript to handle dropdown change event cat(' <script> document.getElementById("competitionDropdown").addEventListener("change", function() { var selectedValue = this.value; var plots = document.querySelectorAll(".plot-div"); plots.forEach(function(plot) { var competition = plot.getAttribute("data-competition"); if (competition === selectedValue) { plot.style.display = "block"; } else { plot.style.display = "none"; } }); }); </script> ') ``` ### Opponent ```{r , echo=FALSE, warning=FALSE, results="asis"} # Create a dropdown menu for opponents cat('<select id="opponentDropdown">') for (opponent in filter_opponent) { cat(sprintf('<option value="%s">%s</option>', opponent, opponent)) } cat('</select>') cat('<div id="plotContainer">') for (i in seq_along(filter_opponent)) { opponent <- filter_opponent[i] cat(sprintf('<div id="plot%d" class="plot-div" data-opponent="%s">', i, opponent)) print(plotter(opponent, combined_df)) cat('</div>') } cat('</div>') # JavaScript to handle dropdown change event cat(' <script> document.getElementById("opponentDropdown").addEventListener("change", function() { var selectedValue = this.value; var plots = document.querySelectorAll(".plot-div"); plots.forEach(function(plot) { var opponent = plot.getAttribute("data-opponent"); if (opponent === selectedValue) { plot.style.display = "block"; } else { plot.style.display = "none"; } }); }); </script> ') ``` ### Matchday ```{r , echo=FALSE, warning=FALSE, results="asis"} # Create a dropdown menu for matchdays cat('<select id="matchdayDropdown">') for (matchday in filter_matchday) { cat(sprintf('<option value="%s">%s</option>', matchday, matchday)) } cat('</select>') cat('<div id="plotContainer">') for (i in seq_along(filter_matchday)) { matchday <- filter_matchday[i] cat(sprintf('<div id="plot%d" class="plot-div" data-matchday="%s">', i, matchday)) print(plotter(matchday, combined_df)) cat('</div>') } cat('</div>') # JavaScript to handle dropdown change event cat(' <script> document.getElementById("matchdayDropdown").addEventListener("change", function() { var selectedValue = this.value; var plots = document.querySelectorAll(".plot-div"); plots.forEach(function(plot) { var matchday = plot.getAttribute("data-matchday"); if (matchday === selectedValue) { plot.style.display = "block"; } else { plot.style.display = "none"; } }); }); </script> ') ``` ### Last Minute Goals ```{r , echo=FALSE, warning=FALSE, results="asis"} # Create a dropdown menu for minutes cat('<select id="minuteDropdown">') for (minute in filter_minute) { cat(sprintf('<option value="%s">%s</option>', minute, minute)) } cat('</select>') cat('<div id="plotContainer">') for (i in seq_along(filter_minute)) { minute <- filter_minute[i] cat(sprintf('<div id="plot%d" class="plot-div" data-minute="%s">', i, minute)) print(plotter(minute, combined_df)) cat('</div>') } cat('</div>') # JavaScript to handle dropdown change event cat(' <script> document.getElementById("minuteDropdown").addEventListener("change", function() { var selectedValue = this.value; var plots = document.querySelectorAll(".plot-div"); plots.forEach(function(plot) { var minute = plot.getAttribute("data-minute"); if (minute === selectedValue) { plot.style.display = "block"; } else { plot.style.display = "none"; } }); }); </script> ') ``` ::: ### Discussion In the second part of the project delves deeper into the goal-scoring prowess of Ronaldo and Messi, examining their performance across various dimensions such as countries, competitions, opponents, matchdays, and goal times. It employs data wrangling techniques, function definitions, and comprehensive analysis to provide a nuanced comparison of their performances, offering insights into their respective careers and contributions to the sport. ### Limitations The comparison between both players spans the available data from 2005 to 2022, covering a duration of 18 years. As the dataset grows larger, computational resources and processing times required for data analysis and visualization may increase significantly, posing scalability challenges, particularly when dealing with extensive historical data or incorporating real-time updates. The project focuses primarily on goal-scoring statistics and may not capture the entirety of Ronaldo and Messi's contributions to football. Other aspects of their gameplay, such as assists, dribbles, defensive actions, etc., are not considered, which may provide a more holistic understanding of their performance.